While these approaches extract rich contextual features from the images, the embeddings from the support set and the query set usually exhibit certain distribution shifts, i.e., the Support-Query Shift [1]}. For example, the images are captured by various devices, e.g., smartphones and single-lens reflex cameras in different environments, e.g., foggy and high-luminance. Since the learned embeddings are located in different spaces, degrading the model performance, optimal transportation [2]} has been proposed to stage the embeddings from different domains into the same latent space. However, in this paper, we theoretically prove that optimal transport can be easily misguided by small perturbations in the images (as illustrated in Fig. REF ).
